Improving healthcare cost prediction for chronic disease through covariate clustering and subgroup analysis methods

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      <subfield code="a">Li, Zhengxiao </subfield>
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      <subfield code="c">Zhengxiao Li, Yifan Huang and Yang Cao</subfield>
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      <subfield code="a">Predicting healthcare costs for chronic diseases is difficult because these costs depend not only on medical factors but also on patients' own perceptions and behaviors. To better capture this complexity, this paper introduces a new statistical framework that combines covariate clustering with finite mixture regression models. This approach groups highly related variables and identifies patient subgroups, improving both model interpretability and prediction accuracy in high-dimensional, noisy, and correlated data settings. The method uses a penalized clustering structure and a dedicated EMADMM algorithm to handle the challenging optimization problem. Through simulations and real-world data from diabetes patients, the framework shows strong stability and effectiveness: it improves predictions by sharing information across related variables and reveals meaningful behavioral patterns in patients' self-perception data.</subfield>
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      <subfield code="g">12/05/2025 Volume 55 Issue 2 - may 2025 , p. 375 - 394</subfield>
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